PROJECT SUMMARY Label-free quantification of analytes using liquid chromatography-mass spectrometry (LC-MS) is gaining recognition as a very good strategy for biomarker discovery. However, such quantification is not addressed adequately in the instrument-specific software packages. In particular, alignment of LC-MS data presents a significant challenge in label-free quantification and comparison of biomolecules. This challenge coupled with biological variability and disease heterogeneity in human populations has restricted recent advances in LC- MS-based biomarker discovery studies. This project brings together experts in bioinformatics, biostatistics, biochemistry, clinical cancer research, chromatography, and mass spectrometry to develop novel analytical tools for LC-MS-based label-free quantification and comparison of glycans and peptides in serum and plasma. Specifically, a novel probabilistic-based mixture regression model and a new clustering-based method will be investigated for alignment of LC-MS data and for identification of patient subgroups. LC-MS data from spike-in studies will be utilized to develop and optimize the proposed alignment methods and to compare their performance with other existing solutions. The optimized algorithms and statistical methods will be applied to identify peptide and glycan candidate biomarkers for early detection of HCC. This will be accomplished by using two LC-MS technologies to evaluate the expression of peptides and glycans in serum and plasma samples collected from HCC patients and cirrhotic controls. The candidate biomarkers will be validated using isotope dilution mass spectrometric assays. Our proposal to perform both peptide and glycan profiling studies based on serum and plasma samples from the same participants is a unique opportunity to explore an integromic approach for marker discovery. Furthermore, this project will capitalize on markers identified in this study and other previous studies to investigate key metabolic and signaling pathways that may be related to the progression of HCC. This will enhance our understanding of the disease progression and the functional involvement of the markers in metabolic and signaling pathways, which could be used to design and test improved treatment strategies.